@article{Gallo:182488,
      recid = {182488},
      author = {Gallo, C. and Contò, F. and Fiore, M.},
      title = {A Neural Network Model for Forecasting CO2 Emission},
      journal = {AGRIS on-line Papers in Economics and Informatics},
      address = {2014-06-30},
      number = {665-2016-45020},
      series = {6},
      pages = {6},
      month = {Jun},
      year = {2014},
      abstract = {Air pollution is today a serious problem, caused mainly by  human activity. Classical methods are not considered able  to efficiently model complex phenomena as meteorology and  air pollution because, usually, they make approximations or  too rigid schematisations. Our purpose is a more flexible  architecture (artificial neural network model) to implement  a short-term CO2 emission forecasting tool applied to the  cereal sector in Apulia region – in Southern Italy - to  determine how the introduction of cultural methods with  less environmental impact acts on a possible pollution  reduction.},
      url = {http://ageconsearch.umn.edu/record/182488},
      doi = {https://doi.org/10.22004/ag.econ.182488},
}